OpenRaiser/Intern-Atlas huggingface dataset:
Garbage Collection in JyNI - How to bridge Mark/Sweep and Reference Counting GC
Venue: arXiv cs.PL 2016
Year: 2016

Jython is a Java-based Python implementation and the most seamless way to integrate Python and Java. It achieves high efficiency by compiling Python code to Java bytecode and thus letting Java's JIT optimize it - an approach that enables Python code to call Java functions or to subclass Java classes. It enables Python code to leverage Java's multithreading features and utilizes Java's built-in garbage collection (GC). However, it currently does not support CPython's C-API and thus does not support native extensions like NumPy and SciPy. Since most scientific code depends on such extensions, it is not runnable with Jython. Jython Native Interface (JyNI) is a compatibility layer that aims to provide CPython's native C extension API on top of Jython. JyNI is implemented using the Java Native Interface (JNI) and its native part is designed to be binary compatible with existing extension builds [...].
$λ$-persistant CSMA: a radio-channel access protocol
Venue: arXiv cs.NI 2016
Year: 2016

This paper presents an algorithm that improves channel-access statistics for wireless medium. The proposed modification of the standard CSMA algorithm is analytically shown to yield better results and simulation results are given to support this claim.
A Jamming-Resilient MAC-layer Device Identification for Internet of Things
Venue: arXiv cs.NI 2016
Year: 2016

In a number of practical scenarios a wireless device needs to mark its presence, for instance, to some access point. That enables the access point to assign the device its transmission slot or update the count of the network nodes. Many protocols can achieve exactly this result. In this paper, our goal is to show how that can be done in the simplest messaging model, the so-called beeping model. Consequently, we constrain our design so that the station does not send any modulated information in the signal and the receiver actually does not need to demodulate/decode it. We are interested in sending just a short signal, so called 'beep'. Moreover, we want to design such protocol that is resilient to random interference and enables us to identify devices which are sending the signal, as opposed to only note their presence. To do that, we leverage temporal correlations of a sequence of beeps issued by a device, as if the time-moments when they happen come from a pre-defined probability distribution, that is the fingerpring of the device.
MDS-Coded Distributed Storage for Low Delay Wireless Content Delivery
Venue: arXiv cs.IT 2016
Year: 2016

We address the use of maximum distance separable (MDS) codes for distributed storage (DS) to enable efficient content delivery in wireless networks. Content is stored in a number of the mobile devices and can be retrieved from them using device-to-device communication or, alternatively, from the base station (BS). We derive an analytical expression for the download delay in the hypothesis that the reliability state of the network is periodically restored. Our analysis shows that MDS-coded DS can dramatically reduce the download time with respect to the reference scenario where content is always downloaded from the BS.
Spatially Coupled LDPC Codes Affected by a Single Random Burst of Erasures
Venue: arXiv cs.IT 2016
Year: 2016

Spatially-Coupled LDPC (SC-LDPC) ensembles achieve the capacity of binary memoryless channels (BMS), asymptotically, under belief-propagation (BP) decoding. In this paper, we study the BP decoding of these code ensembles over a BMS channel and in the presence of a single random burst of erasures. We show that in the limit of code length, codewords can be recovered successfully if the length of the burst is smaller than some maximum recoverable burst length. We observe that the maximum recoverable burst length is practically the same if the transmission takes place over binary erasure channel or over binary additive white Gaussian channel with the same capacity. Analyzing the stopping sets, we also estimate the decoding failure probability (the error floor) when the code length is finite.
Direct Localization for Massive MIMO
Venue: arXiv cs.IT 2016
Year: 2016

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first processed to obtain a user's AOA at different base stations, followed by triangulation to determine the user's position. In the presence of multipath, the performance of these methods is greatly degraded due to the inability to correctly detect and/or estimate the AOA of the line-of-sight (LOS) paths. To counter the limitations of this two-step procedure which is inherently sub-optimal, we propose a direct localization approach in which the position of a user is localized by jointly processing the observations obtained at distributed massive MIMO base stations. Our approach is based on a novel compressed sensing framework that exploits channel properties to distinguish LOS from non-LOS signal paths, and leads to improved performance results compared to previous existing methods.
A fast marching algorithm for the factored eikonal equation
Venue: arXiv cs.CE 2016
Year: 2016

The eikonal equation is instrumental in many applications in several fields ranging from computer vision to geoscience. This equation can be efficiently solved using the iterative Fast Sweeping (FS) methods and the direct Fast Marching (FM) methods. However, when used for a point source, the original eikonal equation is known to yield inaccurate numerical solutions, because of a singularity at the source. In this case, the factored eikonal equation is often preferred, and is known to yield a more accurate numerical solution. One application that requires the solution of the eikonal equation for point sources is travel time tomography. This inverse problem may be formulated using the eikonal equation as a forward problem. While this problem has been solved using FS in the past, the more recent choice for applying it involves FM methods because of the efficiency in which sensitivities can be obtained using them. However, while several FS methods are available for solving the factored equation, the FM method is available only for the original eikonal equation. In this paper we develop a Fast Marching algorithm for the factored eikonal equation, using both first and second order finite-difference schemes. Our algorithm follows the same lines as the original FM algorithm and requires the same computational effort. In addition, we show how to obtain sensitivities using this FM method and apply travel time tomography, formulated as an inverse factored eikonal equation. Numerical results in two and three dimensions show that our algorithm solves the factored eikonal equation efficiently, and demonstrate the achieved accuracy for computing the travel time. We also demonstrate a recovery of a 2D and 3D heterogeneous medium by travel time tomography using the eikonal equation for forward modelling and inversion by Gauss-Newton.
Verifying Reachability in Networks with Mutable Datapaths
Venue: arXiv cs.NI 2016
Year: 2016

Recent work has made great progress in verifying the forwarding correctness of networks . However, these approaches cannot be used to verify networks containing middleboxes, such as caches and firewalls, whose forwarding behavior depends on previously observed traffic. We explore how to verify reachability properties for networks that include such "mutable datapath" elements. We want our verification results to hold not just for the given network, but also in the presence of failures. The main challenge lies in scaling the approach to handle large and complicated networks, We address by developing and leveraging the concept of slices, which allow network-wide verification to only require analyzing small portions of the network. We show that with slices the time required to verify an invariant on many production networks is independent of the size of the network itself.
Facial Expression Classification Using Rotation Slepian-based Moment Invariants
Venue: arXiv cs.CV 2016
Year: 2016

Rotation moment invariants have been of great interest in image processing and pattern recognition. This paper presents a novel kind of rotation moment invariants based on the Slepian functions, which were originally introduced in the method of separation of variables for Helmholtz equations. They were first proposed for time series by Slepian and his coworkers in the 1960s. Recent studies have shown that these functions have an good performance in local approximation compared to other approximation basis. Motivated by the good approximation performance, we construct the Slepian-based moments and derive the rotation invariant. We not only theoretically prove the invariance, but also discuss the experiments on real data. The proposed rotation invariants are robust to noise and yield decent performance in facial expression classification.
Click Carving: Segmenting Objects in Video with Point Clicks
Venue: arXiv cs.CV 2016
Year: 2016

We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest. Whereas conventional interactive pipelines take the user's initialization as a starting point, we show the value in the system taking the lead even in initialization. In particular, for a given video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using image and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2-3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. Finally, the mask is propagated across the video to produce a spatio-temporal object tube. On three challenging datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent balance of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.
Drawing Graphs on Few Lines and Few Planes
Venue: arXiv cs.CG 2016
Year: 2016

We investigate the problem of drawing graphs in 2D and 3D such that their edges (or only their vertices) can be covered by few lines or planes. We insist on straight-line edges and crossing-free drawings. This problem has many connections to other challenging graph-drawing problems such as small-area or small-volume drawings, layered or track drawings, and drawing graphs with low visual complexity. While some facts about our problem are implicit in previous work, this is the first treatment of the problem in its full generality. Our contribution is as follows. We show lower and upper bounds for the numbers of lines and planes needed for covering drawings of graphs in certain graph classes. In some cases our bounds are asymptotically tight; in some cases we are able to determine exact values. We relate our parameters to standard combinatorial characteristics of graphs (such as the chromatic number, treewidth, maximum degree, or arboricity) and to parameters that have been studied in graph drawing (such as the track number or the number of segments appearing in a drawing). We pay special attention to planar graphs. For example, we show that there are planar graphs that can be drawn in 3-space on a lot fewer lines than in the plane.
Some Garbage In - Some Garbage Out: Asynchronous t-Byzantine as Asynchronous Benign t-resilient system with fixed t-Trojan-Horse Inputs
Venue: arXiv cs.DC 2016
Year: 2016

We show that asynchronous $t$ faults Byzantine system is equivalent to asynchronous $t$-resilient system, where unbeknownst to all, the private inputs of at most $t$ processors were altered and installed by a malicious oracle. The immediate ramification is that dealing with asynchronous Byzantine systems does not call for new topological methods, as was recently employed by various researchers: Asynchronous Byzantine is a standard asynchronous system with an input caveat. It also shows that two recent independent investigations of vector $ε$-agreement in the Byzantine model, and then in the fail-stop model, one was superfluous - in these problems the change of $t$ inputs allowed in the Byzantine has no effect compared to the fail-stop case. This result was motivated by the aim of casting any asynchronous system as a synchronous system where all processors are correct and it is the communication substrate in the form of message-adversary that misbehaves. Thus, in addition, we get such a characterization for the asynchronous Byzantine system.
Distortion Bounds for Transmitting Correlated Sources with Common Part over MAC
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates the joint source-channel coding problem of sending two correlated memoryless sources with common part over a memoryless multiple access channel (MAC). An inner bound and two outer bounds on the achievable distortion region are derived. In particular, they respectively recover the existing bounds for several special cases, such as communication without common part, lossless communication, and noiseless communication. When specialized to quadratic Gaussian communication case, transmitting Gaussian sources with Gaussian common part over Gaussian MAC, the inner bound and outer bound are used to generate two new bounds. Numerical result shows that common part improves the distortion of such distributed source-channel coding problem.
Accelerating eigenvector and pseudospectra computation using blocked multi-shift triangular solves
Venue: arXiv cs.MS 2016
Year: 2016

Multi-shift triangular solves are basic linear algebra calculations with applications in eigenvector and pseudospectra computation. We propose blocked algorithms that efficiently exploit Level 3 BLAS to perform multi-shift triangular solves and safe multi-shift triangular solves. Numerical experiments indicate that computing triangular eigenvectors with a safe multi-shift triangular solve achieves speedups by a factor of 60 relative to LAPACK. This algorithm accelerates the calculation of general eigenvectors threefold. When using multi-shift triangular solves to compute pseudospectra, we report ninefold speedups relative to EigTool.
Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
Venue: arXiv cs.LG 2016
Year: 2016

Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants. We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function. Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance.
A configurable accelerator for manycores: the Explicitly Many-Processor Approach
Venue: arXiv cs.DC 2016
Year: 2016

A new approach to designing processor accelerators is presented. A new computing model and a special kind of accelerator with dynamic (end-user programmable) architecture is suggested. The new model considers a processor, in which a newly introduced supervisor layer coordinates the job of the cores. The cores have the ability (based on the parallelization information provided by the compiler, and using the help of the supervisor) to outsource part of the job they received to some neighbouring core. The introduced changes essentially and advantageously modify the architecture and operation of the computing systems. The computing throughput drastically increases, the efficiency of the technological implementation (computing performance per logic gates) increases, the non-payload activity for using operating system services decreases, the real-time behavior changes advantageously, and connecting accelerators to the processor greatly simplifies. Here only some details of the architecture and operation of the processor are discussed, the rest is described elsewhere.
Node-Centric Detection of Overlapping Communities in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.
A Modified Activation Function with Improved Run-Times For Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

In this paper we present a modified version of the Hyperbolic Tangent Activation Function as a learning unit generator for neural networks. The function uses an integer calibration constant as an approximation to the Euler number, e, based on a quadratic Real Number Formula (RNF) algorithm and an adaptive normalization constraint on the input activations to avoid the vanishing gradient. We demonstrate the effectiveness of the proposed modification using a hypothetical and real world dataset and show that lower run-times can be achieved by learning algorithms using this function leading to improved speed-ups and learning accuracies during training.
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
Venue: arXiv cs.AI 2016
Year: 2016

This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI. The approach is to extend recent work on rolling horizon evolutionary planning, which has been shown to work well for single-player games, to two (or in principle many) player games. To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player. The fitness of each individual is evaluated by playing it against a selection of action-sequences from the opposing population. When choosing an action to take in the game, the first action is chosen from the fittest member of the population for that player. The new algorithm is compared with a number of general video game AI algorithms on three variations of a two-player space battle game, with promising results.
On Achievability of an $(r,l)$ Fractional Linear Network Code
Venue: arXiv cs.IT 2016
Year: 2016

It is known that there exists a network, called as the M-network, which is not scalar linearly solvable but has a vector linear solution for message dimension two. Recently, a generalization of this result has been presented where it has been shown that for any integer $m\geq 2$, there exists a network which has a $(m,m)$ vector linear solution, but does not have a $(w,w)$ vector linear solution for $w<m$. This paper presents a further generalization. Specifically, we show that for any positive integers $k,n,$ and $m\geq 2$, there exists a network which has a $(mk,mn)$ fractional linear solution, but does not have a $(wk,wn)$ fractional linear solution for $w<m$.
VideoLSTM Convolves, Attends and Flows for Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

We present a new architecture for end-to-end sequence learning of actions in video, we call VideoLSTM. Rather than adapting the video to the peculiarities of established recurrent or convolutional architectures, we adapt the architecture to fit the requirements of the video medium. Starting from the soft-Attention LSTM, VideoLSTM makes three novel contributions. First, video has a spatial layout. To exploit the spatial correlation we hardwire convolutions in the soft-Attention LSTM architecture. Second, motion not only informs us about the action content, but also guides better the attention towards the relevant spatio-temporal locations. We introduce motion-based attention. And finally, we demonstrate how the attention from VideoLSTM can be used for action localization by relying on just the action class label. Experiments and comparisons on challenging datasets for action classification and localization support our claims.
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Venue: arXiv cs.CV 2016
Year: 2016

Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
Venue: arXiv cs.CV 2016
Year: 2016

Given the prevalence of JPEG compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed DCT coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropriate coefficient values within the indexed bins with the help of signal priors. The challenge thus lies in how to define suitable priors and apply them effectively. In this paper, we combine three image priors---Laplacian prior for DCT coefficients, sparsity prior and graph-signal smoothness prior for image patches---to construct an efficient JPEG soft decoding algorithm. Specifically, we first use the Laplacian prior to compute a minimum mean square error (MMSE) initial solution for each code block. Next, we show that while the sparsity prior can reduce block artifacts, limiting the size of the over-complete dictionary (to lower computation) would lead to poor recovery of high DCT frequencies. To alleviate this problem, we design a new graph-signal smoothness prior (desired signal has mainly low graph frequencies) based on the left eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared to previous graph-signal smoothness priors, LERaG has desirable image filtering properties with low computation overhead. We demonstrate how LERaG can facilitate recovery of high DCT frequencies of a piecewise smooth (PWS) signal via an interpretation of low graph frequency components as relaxed solutions to normalized cut in spectral clustering. Finally, we construct a soft decoding algorithm using the three signal priors with appropriate prior weights. Experimental results show that our proposal outperforms state-of-the-art soft decoding algorithms in both objective and subjective evaluations noticeably.